This paper is in the following e-collection/theme issue:

2016 Special Iss "Optimization, Data Mining, and Statistical Management of Bio-data in Medical Industry Informatization" (2) Electronic Health Records (971)

Published on in Vol 6, No 4 (2018): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9965, first published .
Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods

Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods

Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods

Authors of this article:

Yu Zhang1 Author Orcid Image ;   Xuwen Wang1 Author Orcid Image ;   Zhen Hou1 Author Orcid Image ;   Jiao Li1 Author Orcid Image
Article Authors Cited by (32) Tweetations (1) Metrics

Journals

  1. Li L, Wang P, Yan J, Wang Y, Li S, Jiang J, Sun Z, Tang B, Chang T, Wang S, Liu Y. Real-world data medical knowledge graph: construction and applications. Artificial Intelligence in Medicine 2020;103:101817 View
  2. Sun H, Xiao J, Zhu W, He Y, Zhang S, Xu X, Hou L, Li J, Ni Y, Xie G. Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation. JMIR Medical Informatics 2020;8(7):e17653 View
  3. Li Y, Wang X, Hui L, Zou L, Li H, Xu L, Liu W. Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations. JMIR Medical Informatics 2020;8(9):e19848 View
  4. Hasan A, Levene M, Weston D. Learning structured medical information from social media. Journal of Biomedical Informatics 2020;110:103568 View
  5. Jiang M, Sanger T, Liu X. Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study. JMIR Medical Informatics 2019;7(4):e14850 View
  6. Zhang T, Wang Y, Wang X, Yang Y, Ye Y. Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine. BMC Medical Informatics and Decision Making 2020;20(1) View
  7. Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z, Wang L. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations. JMIR mHealth and uHealth 2019;7(8):e11966 View
  8. Gong L, Zhang Z, Chen S, Yao J. Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining. Journal of Healthcare Engineering 2020;2020:1 View
  9. Xiu X, Qian Q, Wu S. Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study. JMIR Medical Informatics 2020;8(10):e18287 View
  10. Hu D, Zhang H, Li S, Wang Y, Wu N, Lu X. Automatic Extraction of Lung Cancer Staging Information From Computed Tomography Reports: Deep Learning Approach. JMIR Medical Informatics 2021;9(7):e27955 View
  11. Zhang H, Hu D, Duan H, Li S, Wu N, Lu X. A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging. BMC Medical Informatics and Decision Making 2021;21(S2) View
  12. Fang A, Hu J, Zhao W, Feng M, Fu J, Feng S, Lou P, Ren H, Chen X. Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records. BMC Medical Informatics and Decision Making 2022;22(1) View
  13. Qi R, Lv P, Zhang Q, Wu M. Research on Chinese Medical Entity Recognition Based on Multi-Neural Network Fusion and Improved Tri-Training Algorithm. Applied Sciences 2022;12(17):8539 View
  14. Sun Y, Gao D, Shen X, Li M, Nan J, Zhang W. Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study. JMIR Medical Informatics 2022;10(4):e35606 View
  15. de Oliveira J, da Costa C, Antunes R. Data structuring of electronic health records: a systematic review. Health and Technology 2021;11(6):1219 View
  16. Guo W, Lu J, Han F. Named Entity Recognition for Chinese Electronic Medical Records Based on Multitask and Transfer Learning. IEEE Access 2022;10:77375 View
  17. Yang L, Zheng S, Xu X, Sun Y, Wang X, Li J. Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study. JMIR Medical Education 2021;7(4):e24027 View
  18. Wang P, Li Y, Yang L, Li S, Li L, Zhao Z, Long S, Wang F, Wang H, Li Y, Wang C. An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation. JMIR Medical Informatics 2022;10(8):e38154 View
  19. Yang L, Huang X, Wang J, Yang X, Ding L, Li Z, Li J. Identifying stroke-related quantified evidence from electronic health records in real-world studies. Artificial Intelligence in Medicine 2023;140:102552 View
  20. Goenaga I, Andres E, Gojenola K, Atutxa A. Advances in monolingual and crosslingual automatic disability annotation in Spanish. BMC Bioinformatics 2023;24(1) View
  21. Li M, Gao C, Zhang K, Zhou H, Ying J. A weakly supervised method for named entity recognition of Chinese electronic medical records. Medical & Biological Engineering & Computing 2023;61(10):2733 View
  22. Li X, Sun S, Tang T, Lu J, Zhang L, Yin J, Geng Q, Wu Y. Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study. BMC Medical Informatics and Decision Making 2023;23(1) View
  23. Wang L, Zheng Y, Chen Y, Xu H, Li F. Clinical named entity recognition for percutaneous coronary intervention surgical information with hybrid neural network. Review of Scientific Instruments 2024;95(6) View
  24. Shi L, Zhou W, Wu Y, Yuan N, Zang X, Ji Z, Ganchev I. DCM-CNER: A Dual-Channel Model for Clinical Named Entity Recognition Based on Embedded ConvNet and Gated Dilated CNN. IEEE Access 2024;12:97726 View

Books/Policy Documents

  1. Xu H, Liu H, Jia Q, Zhan Y, Zhang Y, Xie Y. Advances in Artificial Intelligence and Security. View
  2. Zhou L, Qu W, Wei T, Zhou J, Gu Y, Li B. Advances in Artificial Intelligence and Security. View